JPS63313088A - Forecasting device for amount of rainfall - Google Patents

Abstract

PURPOSE:To forecast the latest amount of rainfall at short intervals of time by calculating the gravity center point of the rainfall amount and rainfall density from a rainfall amount distribution chart obtained by calibrating raindrop data obtained by a radar rain gauge with rainfall amount data obtained by a ground rain gauge. CONSTITUTION:A forecasting device 3 calibrates the raindrop data from the radar rain gauge 1 with rainfall amount data from the ground rain gauge 2 at prescribed intervals of time to generate a rainfall amount distribution chart at prescribed intervals of time, thereby calculating the rainfall amount gravity center and rainfall density. Then the movement track of the rainfall amount gravity center is found from the difference between rainfall amount gravity center points of adjacent times and moved to the gravity center point of a forecasted object area. Further, the transition track of the rainfall density is found from the difference in rainfall density between adjacent times. Further, the future moving speed forecasted value and rainfall density forecasted value are calculated from the moving speed data on the rainfall amount gravity center point and the transition track of the rainfall density. Here, the mean value of the amount of rainfall in an area traced back on the movement track of the estimated object area by a moving speed forecasted value is found and multiplied by a variation coefficient obtained from the rainfall density forecasted value to obtain the rainfall amount forecasted value of the object area to be forecasted.

Description

DETAILED DESCRIPTION OF THE INVENTION [Object of the Invention] (Field of Application of n Industry, ■-) The present invention relates to a precipitation purification prediction device suitable for rainwater drainage technology for the purpose of preventing flooding caused by rainwater.

(Prior Art) In recent years, as the population has concentrated in cities and the number of houses has become denser and paved roads have become more densely populated, there has been an increase in the amount of rainwater that collects directly into sewer pipes instead of penetrating into the ground. Along with this, rainfall runoff, that is, the time required for rainfall to become a flow rate and flow through sewer pipes, has been shortened, and when there is a large amount of rainfall, flooding of urban areas has also begun to occur.

On the other hand, according to the recent vQ survey, it has been found that rainfall is concentrated in certain areas.

Utilizing rainwater pumps is an effective way to prevent such flooding. In other words, rainfall flows from the surface of the ground to underground sewage, which accumulates in pump wells within the pumping station, and is discharged mainly into rivers by these pumps.

Therefore, as mentioned above, it is necessary to operate rainwater pumps quickly and appropriately by shortening the rain runoff time and concentrating rainwater areas. For this reason, it is necessary to accurately grasp the flow rate of rainwater flowing into the pump well (inflow flow rate).

The inflow flow rate can be determined by inputting the amount of rainfall using the so-called runoff analysis method, especially the urban runoff analysis method that deals with rainfall that directly flows out without penetrating into the ground.By predicting this amount of rainfall, future rainwater pumps can be calculated. can be operated accurately.

Conventionally, it has been known to measure the amount of rainfall during operation of a rainwater pump by, for example, installing a plurality of Ikegami City 61 meters and measuring with these above-ground rain f/l meters.

However, although this conventional example can measure the amount of rainfall at one point over a wide area, it cannot centrally understand the distribution, and therefore it is not possible to understand the rainfall distribution in the target basin of the pump well. There was a problem.

Furthermore, since the above-mentioned conventional example knows the current value of the amount of rainfall, it is not possible to quickly deal with seasonal rain that directly runs off. Therefore, in the conventional example described above, the amount of rainfall is predicted by ``the pump operator observing the weather outside from the window of the control center, observing the black clouds (rain clouds) that have appeared, and making judgments based on his intuition obtained from experience.'' The pumps are operated in order to bring the water level of the pump well to a lower level before the rains begin.

(Problems to be Solved by the Invention) As described above, conventional rainfall prediction cannot accurately grasp rainfall distribution, and also relies on the pump operator's intuition to predict rainfall. The problem is that the pump cannot be operated quickly and appropriately.

The present invention has been made in view of the problem described in F, and it is an object of the present invention to provide a rainfall amount prediction device suitable for rainwater pump operation.

[Structure of the Invention] (Means for Solving the Problems) In order to achieve the above object, the present invention calibrates raindrop data obtained from a radar rain gauge at predetermined intervals with rainfall data obtained from a ground rain gauge. means for creating hourly rainfall distribution maps at predetermined time intervals; means for calculating each rainfall center point and each rainfall density in each created rainfall distribution map; and means for calculating the rainfall amount between adjacent times. Means for calculating each moving speed data of the rainfall centroid point based on each minute of the centroid point, and determining a moving trajectory of the rainfall centroid point from each calculated moving speed data, and a moving trajectory of the rainfall M centroid point. means to obtain a movement locus of the prediction target area by moving parallel to the m6 point of the rainfall prediction target area; In the future () based on the method of determining the density change trajectory and the movement speed data of the rainfall centroid.
means for calculating a predicted moving speed value based on a trajectory of change in the rainfall density, and calculating a predicted moving speed value in the future based on a trajectory of change in the rainfall density; Means for calculating the average value of rainfall M in the area of a point that has gone back by the amount of time, and multiplying the average value of rainfall by the change coefficient calculated from the predicted rainfall density value to determine the measured rainfall value for the prediction target area. It is characterized by having means and.

(Function) A radar rain gauge obtains raindrop data at predetermined time intervals.
This raindrop data is calibrated with rainfall data obtained from a ground rain gauge to create a rainfall distribution map. From each rainfall distribution map, each rainfall centroid point and each l! Three peak density is calculated.

Then, the movement trajectory of the rainfall centroid point is determined from the movement speed data of the rainfall centroid point, and this movement trajectory is translated in parallel to the center point of the prediction target area of the rainfall to obtain the movement trajectory of the predicted target area. .

On the other hand, the trajectory of changes in rainfall density can be determined from the difference data of rainfall density. From this trajectory of change, a predicted value of rainfall density at a certain time in the future can be obtained, and from the moving speed data, a predicted value of moving speed at the same time in the future can be obtained.

Next, the average rainfall value in the area of the predicted travel speed of 4 minutes is calculated from the center of gravity of the prediction target area on the movement trajectory of the prediction target area, and this rainfall average value is calculated from the predicted rainfall density value. The predicted value between rainfalls is calculated by multiplying by the change coefficient.

(Embodiment) FIG. 1 is a block diagram showing the configuration of an embodiment of a device according to the present invention.

In this example, a rainfall prediction curve R is obtained at short intervals by a rainfall prediction device based on raindrop distribution data C and rainfall data E obtained by a radar rain gauge 1 and a ground seedling gauge 2. The rainfall prediction curve R is supplied to the pump operation controller 4. Note that in the figure, 5 does not indicate a data transmitting device, and 6 does not indicate a data receiving device.

The radar rain gauge 1 converts the intensity of anti-01 radio waves, which depends on the amount of raindrops in the air, into rainfall data and outputs the data.

This radar rain gauge 1 is used to obtain a wide range of rainfall distribution in order to understand the above-mentioned seasonal rainfall concentration.

For example, one piece of rain distribution data (mesh data) in a mesh divided into tens of thousands of areas with a radius of several hundred kilometers is output at predetermined time intervals of about 5 or 10 minutes.

That is, it is assumed that the time interval is 8 minutes, the time is expressed as a discrete time, and the rainfall forecasting device H3 is activated at time Ko +1 >K>Ko. By that time, (Kp+1) sets of rainfall FrS data are obtained from =Ko KE+ (Kp≧2) at a certain time in the past to the present (Kp+1).

Figure 2 shows an example of a mesh obtained by the radar rain gauge 1, in which rainfall data is obtained for each mesh (for example, several kilometers square) (a mesh with zero rainfall is indicated as O). There is. In the figure, 7 is the target basin for rainfall collected by the sewage pipe network, 8 is the pump station installed in the pump well in target basin 7, and 9 is the river from which the wastewater from pump station 8 flows out. .

The ground rain gauges 2 are used to correct the rainfall data obtained by the radar 1, and N pieces are installed over the area including the target watershed 7. Rainfall data (rainfall surface distribution data) Ei (i =
1.2. ..., N) are supplied to the rainfall amount prediction device 3.

The rainfall forecasting device 43 uses data E obtained from the above-mentioned ground rain gauge 2 to determine its correlation with the radar rain flst1 in events prior to the current rainfall event, as shown in FIG. 3. In addition, the number of meshes is usually tens of thousands, which is much larger than the number N of the above-ground mountain measurement 2, and the above-ground mountain A relationship is obtained individually for each Mt total 2. This rainfall prediction device performs rainfall prediction curves as shown in the flowcharts of FIGS. 4 and 5.

As shown in FIGS. 4 and 5, the processing executed by the above-mentioned fermentation prediction device is roughly divided into four parts.

The first part of the processing is the individual L! for each of the past (Kp+1) sets of in-plane data. It is FJ. Furthermore, the second part is in-plane data processing that takes place between adjacent times. Furthermore, the third part is a process that simultaneously handles the past (Kp+1> sets of data.The fourth part is a prediction performance of KF rainfall events up to time (Ko+KF>).

In the first part, processing is performed sequentially from time Ko to Kfl. First, data Ei (i = 1.2°..., N) and the corresponding mesh data group Ci (there are tens of thousands of mesh data, which were divided into N pieces. i = 1.2
．． ..., N), the mesh data of the distribution map is corrected, and a rainfall ω distribution map Dke is created.

Next, the rainfall distribution map created. Find the center point QkO(xl(,,Vgo) of the rainfall fMffi.This differs from a simple centroid in that, for a plane composed of meshes where the amount of rainfall is not 0, the amount of rainfall as an attribute of the mesh is treated as the mesh weight. In addition, if the number of meshes whose rainfall is not O is rnl<, then the sum of the areas is 30 = m, where the area of the unit mesh is S u = X, A・VQ. e xu * V, A, and the rainfall amount of the mesh data Ij u = 1.2..., m
g. ) is expressed as (m,.・×1・ytt).

Next, the raindrop distribution map C1-1 at time (Ko-1) is read out, and the above-mentioned calculations are performed to create a rainfall distribution map Dico-+, calculate the gravity center point Gko-l of the rainfall m, and calculate the rainfall density. R.K.
Each process of calculating o-I is executed.

In this way, the raindrop distribution map cK at time (Ko- to ρ)
At each time from 6 to 9, the creation of the rainfall distribution map DK, the calculation of the center of gravity of the rain outlet GK (7), and the calculation of the rainfall density R are executed.

In the second part, variations are calculated for two types of processed data pairs at adjacent times. First, calculate the moving distance between the center of gravity GKo and GKo-1 of the rainfall m at the adjacent times Ko and Ko-1, that is, the moving speed ■K, and then calculate the amount of rainfall iR. and R.K.
Oji's difference, △Rk, is mentioned.

Since this operation is performed on pairs of data at adjacent times using (Kp+1) pieces of data, Kp pieces of data are obtained.

The third part is a calculation for grasping the characteristics of the dynamic behavior of the rainfall amount from time Ko to time Ko-KD.

In order to predict the direction of movement of the rain area, first, we calculate the rainfall centroid point GK as a point that represents the rain area.
The locus of movement of the center of gravity is understood as a straight line, and it is thought that the center of gravity will move along that straight line in the near future. In this case, it is assumed that emphasis is placed on the centroid point GK6 of -Ko at the current time, and as the time goes back, the importance level decreases, and the method of least squares is applied using a weighting factor called a rarefaction factor (1: orgetting factor). do.

In other words, 2 of the perpendicular distance from the center of gravity G2 to the straight line f
A straight line that minimizes the sum of (Kp+1) from time 0 to time Ko-Kp by multiplying the power by the rarefaction coefficient ωl'ce-K! , determine. Once this straight line f~ is obtained, next, a parallel straight line fB passing through the surface center of gravity PKO of the target watershed 7
Create as shown in Figure 6.

That is, it is thought that the rain area will move along this direct FilfB for several tens of minutes in the near future. The moving speed is determined by connecting the characteristics of changes in past data V3, and fitting a curve /v representing the characteristics, that is, applying the method of least squares to the curve.

In this case, the order of the curve depends on the value of time Kp, but −
There are several types, such as second order, second order, and third order, and from among them, the better order is selected using the evaluation index AIC (Akaike Information Needle Criterion). Similarly, curve fitting is performed for the rainfall density difference ΔRK to obtain the curve IIf.

The fourth part is the rainfall gauge section. The time to predict is several hours, and here it is assumed that the time is until =Ko +K'' at the release time.

First, preparations are made for predicting rainfall m. The moving speed vk during the prediction period is determined by extrapolating the curve j'v and reading 4tiVKo++ at time (Ko+1>) as shown in Figure 7.Also, for the rainfall density difference ΔRK, as shown in Figure 8, the curve fa Extrapolate the time (K.

It is assumed that these two values do not change during the prediction period.

The above is the first part of the prediction work, and after reading out the latest rainfall distribution map DKo and assuming that the relationship between the mesh data does not change, the next part 21 begins.

First, the predicted trajectory where the rain area will come above the target watershed 7 is on the straight line fB, and the moving speed is VKoffl, so the center of gravity of the target watershed PK is the base point, and the speed ■K
The upstream point P is just a few steps back. Take 1q of the old one. The average amount of rainfall in a mesh having the same shape as the mesh group forming the target watershed 7 centered around the mesh having this point PKo+l inside is set as vk-off. here,
Considering that the hourly rainfall density difference changes by the value △R3゜+1, correct the change coefficient and turn this off to the rainfall IR.
shall be. Similarly, in order to obtain the predicted rainfall value RKo+1 at time Ko+2, base point P4+1 on the straight line fB
From speed ■. Only the old point gets +2 to the upstream point P,
Perform the process described above. Repeating this operation, time Ko+KF
By obtaining this, the calculation ends. As shown in FIG. 9, the output is the entire rainfall amount curve, that is, the rainfall amount curve R consisting of (Kp +1+KF) pieces of data in the form of a combination of actual values and predicted values.

By passing the output of the prediction device 3 to the operation@station 4, the device 4 can perform outflow analysis and predict the inflow flow rate curve. Since it is possible to configure an operation algorithm that combines this inflow flow rate and pump well water level tl data, it is easy to determine the pump discharge rate depending on the inflow flow needle, and furthermore,
MU4 also adjusts the water level from the pump well water level to the specified target water level.
11. Be able to do it.

Note that radar rain gauge 1 has an observation area with a radius of several hundred kilometers and a mesh with a side of several kilometers, but in order to increase accuracy, a mesh with a radius of several hundred meters on a side and an observation area of several kilometers in radius is also used. It is possible. It is also possible to use these two types of radar rain gauges at the same time. In this case, coarse mesh data is used to calculate the first to third parts of Figures 4 and 5, and the fourth part is calculated. If we apply the mesh data every time we read out the seasonal rainfall ω distribution map Dk0, the rainfall prediction fi
It becomes possible to make aRK more accurate.

[Effects of the Invention] As explained above, according to the present invention, it is possible to predict the latest rainfall amount at short intervals using only raindrop distribution data from a radar rain gauge and rain shelter data from a ground rain gauge. It becomes possible.

As a result, by using it in combination with a pump operating device that has the calculation function of the runoff analysis method, it is possible to output short-time interval rainfall curves up to several hours of light from radar rain gauge data and ground mountain base meter data. It becomes possible to calculate the outflow analysis method using this curve data as input, and the inflow flow rate curve can be highlighted.

This provides the following advantages.

In other words, it is possible to configure an algorithm to operate the pump according to the original inflow flow rate, and in addition to the 1 water level of the bomb, it is possible to use 2 variable suspensions and prepare a variety of operation algorithms that take into account various aspects. , it is possible to adopt a driving method that is suitable for the problem that needs to be addressed. An example of this is the control of the number of pumps u, which can realize a safe method in which the number of pump operations is changed to an extremely small number.

In addition, since rainfall can be predicted up to several hours of light, changes in pump well water level due to pump operation can be detected over several hours of light, making it possible to perform prompt treatment depending on the heart chamber.

[Brief explanation of drawings]

FIG. 1 is a block diagram showing the configuration of an embodiment according to the present invention, FIG. 2 is an explanatory diagram of mesh data obtained by a radar rain gauge, FIG. 3 is an explanatory diagram of the relationship between the radar rain gauge and the ground rain gauge, and FIG. 4 and 5 are 9! executed by the rainfall forecasting device of the present invention. Figure 6 is an explanatory diagram of the procedure for creating a predicted trajectory of the hypocenter of the rainfall area that passes through the center of gravity of the target watershed, and Figure 7 is the movement of the center of the Shigure type 1 type. FIG. 8 is a diagram showing a prediction curve of speed, FIG. 8 is a diagram showing a prediction curve of hourly rainfall density change, and FIG. 9 is a diagram showing an output example of the rainfall prediction device according to the present invention. 1... Radar rain gauge 2... Ground mountain is old 3... Rainfall prediction device

Claims (1)

[Scope of Claims] Means for creating a rainfall distribution map at each predetermined time interval by calibrating raindrop data obtained from a radar rain gauge at predetermined time intervals with rainfall data obtained from a ground rain gauge; means for calculating each rainfall centroid point and each rainfall density in a rainfall distribution map; calculating each movement speed data of the rainfall centroid point based on each difference of the rainfall centroid point between adjacent times; means for determining a movement trajectory of a rainfall gravity center point from each calculated movement speed data; means for calculating each difference data of the rainfall density between adjacent times, and determining a change locus of rainfall density from each calculated difference data; means for calculating a predicted moving speed value and a predicted future rainfall density value from a trajectory of change in the rainfall density; means for calculating an average rainfall amount in a region of a point that is traced back by the amount of time; and means for calculating a predicted rainfall value for a prediction target area by multiplying the determined average rainfall value by a change coefficient determined from the predicted rainfall density value; A rainfall forecasting device comprising: